$$\text{Forecasting Greenhouse Gases in Algeria}$$

$$\text{Abstract}$$

For the past 20 years, Assekrem station located in the heights of Huggar in Southern Algeria was taking measurements of the most prominent greenhouse gases in the atmosphere as part of NOAA’s global monitoring initiative. This paper aims to develop models that best fit the trends highlighted in data using Facebook’s Prophet forecasting engine. Furthermore, we present a concise explanation of the periodic patterns in greenhouse gas emissions, their sources, sinks, and their global warming potential. Finally, we discuss the current abundance of the gases in the atmosphere and forecast their alarming levels.

$\text{Introduction}$

The station is located on the summit (plateau) of the second highest point of the Hoggar mountain range in the Saharan desert. The site is very remote at a distance of 50 km from Tamanrasset. Touristic activities in the area are limited due to difficult access to a few dozen visitors per day. Vegetation is extremely sparse.

$\text{Data}$

The Assekrem station allows us to get a clean read with little local bias due to the remoteness of the location. This allows us to get atmospheric readings of the specific gases as opposed to local short-lived and erratic gas emissions. For example, CO2 travels and mixes in the atmosphere due to its long lifetime, allowing us to see this value as opposed to having a station right next to an industrial city, where readings will largely depend on the production for that specific factory. The high position of the station can indicate that the readings are safe from biases induced from the thermal inversion layer of the atmosphere, including breeze, wind changes, and reduced or enhanced molecular transport. This provides a sturdy ground for atmospheric readings.

$\text{Limitations and strengths of the data}$

The data is collected in the Assekrem station in Algeria. Located at 23.2625° North, 5.6322° East, and at a 2710 meter elevation, this is one of the most remote stations and one of the only ones in the entire Saharan region. Many different techniques are used to monitor gases on Earth, but all have some type of limitations. For this specific case, the station uses in-situ measurements, which involve a single location using high quality, wide set of instrumentation, which can take precise measurements of a small and specific geographical point. two main limitations arise from this method:

  • Lack of certainty for generalizations as we only look at one location
  • Gaps between measuring sties make understanding complex processes difficult

In situ measurements provide what is best described as direct observations of the system. The great advantage is the way we can make use of a diverse set of instrumentation which can take the most accurate readings we can get.

$\text{Methodology}$

Prophet is an algorithm developed by Facebook’s data team in order to automate a routine for forecasting trends given equally spaced data points (Taylor et. al, 2017). The strategy is to frame the forecasting problem as a curve-fitting by disentangling two components:

  • Non-linear trends: Using a logistic growth model containing a time varying capacity and growth rate.
  • Periodic seasonalities: Using a standard Fourier series for decomposing periodic patterns such as yearly, monthly, and daily seasonalities.

The parameters of the model are adjustable (eg., adding seasonalities, specifying forecast frequency, etc) which offers us the advantages of the Bayesian approach since the forecasts are derived from the posterior distribution.

In [1]:
from fbprophet import *
import pandas as pd
import plotly.offline as py
import plotly.graph_objs as go
In [2]:
def forecasting(gas_name, gas_code, months, unit):
    gas_data = pd.read_table('C:/Users/Taha/Desktop/algeria_ghg/'\
               +gas_code+'_ask_surface-flask_1_ccgg_month.txt',
               sep='\s{1,}', names=['stations','year','month','y'], engine='python')
    gas_data['ds'] = pd.to_datetime(gas_data[['year', 'month']].assign(DAY=1))
    
    model = Prophet(weekly_seasonality=False, daily_seasonality=False)
    model.set_auto_seasonalities
    model.add_seasonality(name='monthly', period=30, fourier_order=5)
    model.fit(gas_data)
    future = model.make_future_dataframe(periods=months,freq='M')
    forecast = model.predict(future)
    py.init_notebook_mode()
    fig1 = plot.plot_plotly(model, forecast)
    fig1.update_layout(title=str(gas_name)+' Forecast', xaxis_title='Time', 
                      yaxis_title='Parts per '+unit+'illion (PP'+unit+')')
    py.iplot(fig1)
    
    fig2 = plot.plot_components_plotly(model, forecast)
    fig2.update_layout(title=str(gas_name)+'<br>Trend & Seasonality', height=500)
    py.iplot(fig2)

$\text{Carbon Dioxide}$

  1. $\textbf{Gas measurements oscillations}$
    2. CO2 data points show a distinct seasonality in the measurements. Most of the landmass on Earth lies on the northern hemisphere, and when we look closely at the data, we are able to see that the smaller downward trends coincide with northern hemisphere spring and summer and the upward trend coincides with autumn and winter. This is because of the natural carbon cycle, often referred to as Earth’s breathing “breathing”. Spring and summer bring increased plan life, allowing them to remove carbon from the atmosphere, acting as a strong sink for CO2. On the other hand autumn and winter represent the demise of many of these seasonal plants removing the carbon sink, and releasing organic carbon found in plants to the atmosphere.

  2. $\textbf{Sink and sources}$
    3. Human activity is one of the primary drivers of CO2 increase over time. CO2 exists in the atmosphere in a balance between generation and removal of the gass, driven by different processes. Common sources of CO2 include natural and anthropogenic activity, namely, respiration, decomposition, volcanism, industrial activity, and transportation. More specific to the station’s region, we see strong human activity stemming from transportation and the power sector. Equally important, the sinks represent a way for the environment to deposit, remove, or disperse a specific chemical in the atmosphere. Some of the most important sinks of CO2 include outward transport, chemical removal, ocean absorption, soil deposition, and plant respiration. Broadly speaking, the most active carbon pools on land are living biomass and soil organic carbon. Taking into consideration the region, we have to account for desert sinks, which are limited due to the lack of vegetation, however, desert basins also act as carbon sinks and store it underground. As explained in Li et al. (2015), dissolved inorganic carbon is leached from irrigated arid land and deposited in saline/alkaline aquifers found under the desert. Since this region has limited local sources and sinks of carbon, we can expect to regard transport as one of the main drivers of data patterns in the region having industrial activity and ocean absorption as some of the most significant balancing processes in action.

  3. $\textbf{Important facts}$
    4. CO2 has a long lifetime, which is one of the biggest reasons why it is such a powerful greenhouse gas, and is present globally, as opposed to locally isolated over industrial areas. This long lifetime means that identifying specific sources for this station is quite complex, specifically since large urban areas are not particularly close to it. Wind patterns are particularly important for high CO2 levels in this region as they blow industrial activity into the desert from coastal cities in North Africa and even Europe. These distances are possible due to the long lifetime of CO2 in the atmosphere.

  4. $\textbf{Global Warming Potential (GWP)}$
    5. The Global Warming Potential (GWP) indicator relates to the heat absorbed by a greenhouse gas in the atmosphere. GWP depends on the following factors: - The absorption of infrared radiation by a given gas - The spectral location of its absorbing wavelengths - The atmospheric lifetime of the gas Being the most prominent greenhouse gas in terms of quantity, CO2 is set as the baseline for GWP with a given value of 1. This means that other gases are measured as multipliers if we were to have the same amount. This information is relevant to understanding the GWP of the other gases targeted in this report.

  5. $\textbf{Current state}$
    6. In 2018, the global average carbon dioxide concentration was 407.4 ppm. This is the highest value of CO2 over the past 800,000 years. Since we are concerned with the anthropogenic impact on CO2 concentrations, it's most relevant to look at stable pre-industrial carbon concentrations, which stand at around 280 ppm. Given human activity, it is clear from data collected in this station that there is an upward trend that extends all the way since the start of the industrial revolution. In modern times, levels continue to accelerate with the rise of developing industrial economies. CO2 is a naturally occurring and necessary gas for conserving the conditions needed for a warm habitable planet, however humans have changed the natural course drastically, threatening to accelerate natural processes such as sea level rise, melting ice caps, and climate change.
In [3]:
'''
Measurements are reported in units of 
micromol/mol (10^-6 mol CO2 per mol of dry air or parts per 
million (ppm)). Measurements are directly traceable to the 
WMO X2007 CO2 mole fraction scale.
'''
forecasting('Carbon Dioxide', 'co2', 240, 'M')

$\text{Methane}$

In [4]:
'''
Measurements are reported in units of nanomol/mol 
(10^-9 mol CH4 per mol of dry air (nmol/mol) or parts per billion 
(ppb)) relative to the NOAA 2004A CH4 standard scale.
'''
forecasting('Methane', 'ch4', 240, 'B')

$\text{Carbon Monoxide}$

In [5]:
'''
Carbon monoxide mixing ratios in these files are reported 
in units of nmol/mol (10^-9 mole CO per mole of dry air 
or as part per billion by mole fraction (ppb)) relative
to the NOAA/WMO CO scale (Novelli et al., 1991, Novelli 
et al., 2003).
'''
forecasting('Carbon Monoxide', 'co', 240, 'B')

$\text{Nitrous Oxide}$

In [7]:
'''
N2O measurements are reported in units of nanomol/mol (10^-9 mol N2O 
per mol of dry air (nmol/mol) or parts per billion (ppb)) relative 
to the NOAA 2006A N2O standard scale.
'''
forecasting('Nitrous Oxide', 'n2o', 240, 'B')

$\text{Sulfur Hexafluoride}$

In [8]:
'''
SF6 measurements are reported in units of picomol/mol (10^-12 mol 
SF6 per mol of dry air (pmol/mol) or parts per trillion (ppt)) 
relative to the NOAA 2014 SF6 standard scale.
'''
forecasting('Nitrous Oxide', 'sf6', 240, 'T')

$\text{Data Source}$

  • National Oceanic and Atmospheric Administration (NOAA)
  • Earth System Research Laboratory (ESRL)
  • Global Monitoring Division (GMD)
  • Carbon Cycle Greenhouse Gases (CCGG)

$\text{Data Source}$